1. Technical Field
The present invention relates to statistical process analysis and more particularly, to analyzing software defects handling processes using multiple statistical metrics.
2. Discussion of the Related Art
The software engineering industry invests today significant resources in Sustaining Engineering and Maintenance (SEM) which accounts to the process of continuing engineering and technical support following a release of a new product. A major part of this effort is focused on issues that arise when a customer reports a problem or a defect in a software product, or a product defect is discovered internally. When this occurs, a Problem Management Report (PMR) is opened. Most PMRs can be solved without changing the product code but some need the code change, which demands a special kind of defect handling process.
Defect handling time is a key metric that measures efficiency of the SEM processes and strongly affects customer satisfaction. Hence, keeping this metric under control and evaluating its trends is very important. Statistical analysis shows that defect handling times are heavy-tailed, i.e., there is a non-negligible probability to observe very long handling times. Such observations can significantly affect the sample mean. Therefore, distinguishing between “random noise” and statistically significant metric changes becomes an important challenge. More particularly, in heavy tailed distributions, statistically methods that are based on mean handling times or normal assumptions are unreliable.